Artificial intelligence is perhaps a "catch all" phrase that grasps within its meaning the concept of a man made machine that thinks, learns, reasons and answers or acts much like a human being or other animal in responding to external stimuli. In effect, this artificial intelligence is the engineer's attempt to at least in part mimic the capability of the human brain. The development of the high speed large memory low cost electronic digital computer, which makes use of micro miniaturized electronic components, permits design of electronic systems that in the general sense mimics a small part of the human brain. For example, computer programs exist that performs calculations, such as by multiplying two numbers and displaying the correct results, just as one would do by rules set in the mind. The computer, however, is quicker and usually more accurate. In reality the computer is not thinking in the normal sense of the word. It is merely performing a set of additions according to a rule or plan which the computer and/or software designer has installed. Thus by reason of that program, one is always assured of a correct result in something simple, such as a calculation, and the power of those modern computers lies in its speed and ability to handle large quantities of numbers quickly. Despite the size and speed of computers, the engineering world concedes that the largest computer today cannot perform the same reasoning and is not equal in ability to the human mind. The difference between designing a circuit or program so that it always produces a correct or best result for a given problem and the manner in which the human mind operates has been recognized by John Hopfield of the California Institute of Technology as reported in the Electronics Engineering Times dated Apr. 7, 1986, pages 53 and 62, 63. Hopfield reasoned that overall behavior, in a particular computation, may be a collective effect of having a vast number of nerve cells, rather than as a biological design objective. A human's mechanical, spectral, thermal, chemical and energy inputs are made to the many thousands of nerve endings, called the dendritic tree, which are attached to the soma and are outputted through axons to axon terminals. The theory is that the group of biological neurons interact and through a process, not exactly understood but theorized, learns. This learning is in some way stored within the vast bundle of nerves.
The neuron consists of several distinct portions, the dendritic tree consisting of a system of branching dendrites from many synapses; the body of the neuron, the soma; and the axon system. Energy or information arrives at one or more of the dendritic inputs and during the transition through the neuron the amount of energy is modified by excitatory or inhibitory post-synaptic potentials.
Researchers in this field reported at the second annual Neural Circuits for Computing Conference, 16-17 April 1986 and reported in Electronic Engineering Times, dated Apr. 21, 1986 on page 14, to use variable resistances to simulate and serve as the synaptic connections. As the response to the excitatory or inhibitory post-synaptic potentials, this method modifies the energy in an analog manner. According to the published literature, researchers at the California Institute of Technology (1984) provide programmable interconnections to emulate positive and negative resistance. Other researchers at AT&T Laboratories use an iron beam to create variable resistors. They grow resistors from amorphous silicons sandwiched between two layers of tungsten. Researchers at MIT vary the threshold of metal nitride oxide semiconductors to control the number of electrons in a packet of charge.
According to Hopfield's theory, Hopfield, J. J., "Neural networks and physical systems with emergent collective computational abilities", Proc. Nat. Acad. Sci. USA 79, 2554-2558, April, 1982, the individual details of neuron behavior are less important than their collective effects. Neurons put out dendritic trees with between 1,000 and 10,000 branches each. "Feedback loops" also seemed significant. Neurons establish mutual connections with their neighbors and communicate with one another simultaneously. According to Hopfield "There seemed to be a high degree of mutual communication going on at a given level in a neural network".
In this first simulating structure, operational amplifiers were chosen to represent the neuron. A square matrix of wires with resistive links at intersections was chosen to represent the interconnection of neurons. Outputs from each amplifier were fed back to the inputs of all the other amplifiers by the interconnection matrix. Hopfield discovered that a symmetrical arrangement of resistances in the interconnection to reach a smooth, regular energy surface that could be used to predict the behavior of the circuit. When disturbed from an equilibrium point on the surface by placing voltages on input points, the circuit would slide down the energy surface to the nearest minimum. The circuit could be designed so that these minimal points define computational output in digital form and could be designed to accept either analog or digital inputs. According to the literature this kind of electronic circuit provides a good solution, but not always the best solution, and in that way better simulates a real life intelligence.
As the same literature reports the positive resistances are very difficult to build in silicons and negative resistances are virtually impossible.
The present invention achieves "feedback" between the block, simulating a neuron, digitally. An advantage is that the digital form of artificial neuron may be fabricated using existing very large scale integrated circuit technology, without the need for new process development of the kind reported in the literature. A further advantage is that a fully implemented artificial neural system containing several hundred neuron elements and several thousand interconnections can be produced on a single VLSI chip. The values of the synaptic connections can readily be increased or decreased; whereas the variable resistance approach reported in the Electronic Engineering Times, Apr. 21, 1986, page 14 and at the second annual Neural Circuits for Computing Conference, 16-17 April 1986 is difficult. A further advantage of the present invention is that upon solving an artificial intelligence problem, the solution is stored in the simulated synaptic connection. That connection can be addressed in an identical manner to a semiconductor memory, then the pattern may be copied to other VLSI neuron systems without the need for the learning process.
An object of the invention is to provide an improved artificial neural system. A further object of the invention is to provide a design that simulates a neural system and that may be fabricated using any existing standard semiconductor or VLSI technology.